Systems and methods for contagious illness surveillance and outbreak detection

ABSTRACT

Systems and methods for population health surveillance utilizing a network of smart thermometers is provided. Based on the geolocated user data provided by the smart thermometers, contagious illness can be forecasted for various population nodes. Population nodes can be provided at various levels of granularity. Geographic or population specific early warning signals can be generated based on detected outbreaks of contagious illness.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. patent application Ser. No.62/991,074 filed on Mar. 18, 2020; U.S. patent application Ser. No.62/991,472 filed on Mar. 18, 2020; and U.S. patent application Ser. No.63/082,288 filed on Sep. 23, 2020 the disclosures of which are eachincorporated herein by reference in their entirety.

BACKGROUND

Infectious diseases continue to be one of the greatest public healthconcerns across the globe. The economic burden of seasonal influenzaamounts to $87.1 billion each year in the United States alone, despitewidespread public attention and the billions invested in preventativemeasures. The United States and other countries, however, lack reliablesignals to rapidly identify developing infectious disease hotspots. TheCenters for Disease Control and Prevention (CDC) uses two major systemsfor epidemic surveillance: (1) the National Notifiable DiseasesSurveillance System (NNDSS), through which local and state healthdepartments send CDC data for about 120 diseases that are predominantlydiagnosed via laboratory confirmation; by definition, this is unable todetect novel illnesses outside of the diseases routinely sent and is alagging indicator due to reliance on existing testing infrastructure,and (2) ILI-Net (with COVID-19-Like Illness, or CLI, tracking), which isbased on outpatient tracking, so is delayed by reporting lags and biasedby changes in care-seeking behavior and differentiated access to care.Additionally, outpatient tracking only captures patients who engagedwith the healthcare system, potentially missing the vast number ofpatients with mild or asymptomatic cases. Not only lagging andincomplete, these metrics also vary across 50 states, creating blindspots for leaders who are making decisions that affect millions ofpeople. Thus, while various entities provide modeling and forecastingfor influenza-like illness (ILI), such ILI forecasts typically have alead time of less than 4 weeks, often do not include geographicgranularity, and are based on lagging data sets. Further, even in viewof an accurate ILI forecast, the unpredictable threat of epidemic orpandemic illnesses, such as COVID-19, or the rapid emergence orre-emergence of diseases like Zika and Ebola are often difficult toquickly identify and target to allow for rapid response andintervention.

BRIEF DESCRIPTION OF THE DRAWINGS

It is believed that certain embodiments will be better understood fromthe following description taken in conjunction with the accompanyingdrawings, in which like references indicate similar elements and inwhich:

FIG. 1 schematically illustrates an end-to-end illness data collectionand processing system in accordance with one non-limiting embodiment.

FIG. 2 depicts a plot showing observed influenza-like illnesses for ageographic area based on the real-time data collected by the illnessdetection and tracking computing system of FIG. 1.

FIGS. 3-4 depict example illness detection and tracking computingsystems in accordance with various non-limiting embodiments.

FIG. 5 schematically illustrates detection of a contagious illnessoutbreak for a geographic region in accordance with one non-limitingembodiment.

FIG. 6 depicts example processing of information that is received from aplurality of temperature sensing probes by an illness detection andtracking computing system.

FIGS. 7-9 provide example visualizations generated by an illnessdetection and tracking computing system in accordance with onenon-limiting embodiment.

FIG. 10 is a flow chart of an example process that can be performed byan illness detection and tracking computing system in accordance withone non-limiting embodiment.

DETAILED DESCRIPTION

Various non-limiting embodiments of the present disclosure will now bedescribed to provide an overall understanding of the principles of thestructure, function, and use of systems, apparatuses, devices, andmethods disclosed. One or more examples of these non-limitingembodiments are illustrated in the selected examples disclosed anddescribed in detail with reference made to FIGS. 1-10 in theaccompanying drawings. Those of ordinary skill in the art willunderstand that systems, apparatuses, devices, and methods specificallydescribed herein and illustrated in the accompanying drawings arenon-limiting embodiments. The features illustrated or described inconnection with one non-limiting embodiment may be combined with thefeatures of other non-limiting embodiments. Such modifications andvariations are intended to be included within the scope of the presentdisclosure.

The systems, apparatuses, devices, and methods disclosed herein aredescribed in detail by way of examples and with reference to thefigures. The examples discussed herein are examples only and areprovided to assist in the explanation of the apparatuses, devices,systems and methods described herein. None of the features or componentsshown in the drawings or discussed below should be taken as mandatoryfor any specific implementation of any of these apparatuses, devices,systems or methods unless specifically designated as mandatory. For easeof reading and clarity, certain components, modules, or methods may bedescribed solely in connection with a specific figure. In thisdisclosure, any identification of specific techniques, arrangements,etc. are either related to a specific example presented or are merely ageneral description of such a technique, arrangement, etc.Identification of specific details or examples are not intended to be,and should not be, construed as mandatory or limiting unlessspecifically designated as such. Any failure to specifically describe acombination or sub-combination of components should not be understood asan indication that any combination or sub-combination is not possible.It will be appreciated that modifications to disclosed and describedexamples, arrangements, configurations, components, elements,apparatuses, devices, systems, methods, etc. can be made and may bedesired for a specific application. Also, for any methods described,regardless of whether the method is described in conjunction with a flowdiagram, it should be understood that unless otherwise specified orrequired by context, any explicit or implicit ordering of stepsperformed in the execution of a method does not imply that those stepsmust be performed in the order presented, but instead may be performedin a different order or in parallel.

Reference throughout the specification to “various embodiments,” “someembodiments,” “one embodiment,” “some example embodiments,” “one exampleembodiment,” or “an embodiment” means that a particular feature,structure, or characteristic described in connection with any embodimentis included in at least one embodiment. Thus, appearances of the phrases“in various embodiments,” “in some embodiments,” “in one embodiment,”“some example embodiments,” “one example embodiment, or “in anembodiment” in places throughout the specification are not necessarilyall referring to the same embodiment. Furthermore, the particularfeatures, structures or characteristics may be combined in any suitablemanner in one or more embodiments.

In accordance with various embodiments of the present disclosure, userdata obtained from a network of temperature sensing probes (sometimesreferred to as “smart thermometers”) can be leveraged to identifyincreased transmission and growth in the number of people that are sick.In some cases, the network of temperature sensing probes includeshundreds of thousands, or even millions, of temperature sensing probesthat are each collecting user data from various geographic regions orother types of population nodes. Beneficially, due to the minimizationof lag between the data collection and processing, as well as themassive volume of user data received from the network of temperaturesensing probes, such identification of increased transmission and/orgrowth can occur well before the established healthcare system couldeven potentially detect similar metrics.

By way of explanation, the healthcare system does not gain data frommildly symptomatic people who never go to a doctor, hospital, or a labbecause their symptoms are treatable at home or resolve withoutrequiring medical attention. Furthermore, underserved populations may beless likely to visit a doctor, a hospital, or a lab for mild or evenmore severe illness due to cost of care and barriers to accessing care.Underserved populations are often at an increased risk of experiencinginfectious disease due to factors such as more crowded livingconditions, higher contact-intensity jobs, and more limited access tohealthcare resources. The presently disclosed systems and methods areable to ingest data from these underserved communities which areunderrepresented up by current healthcare data. Moreover, conventionalhealthcare data is too delayed to provide similar insights as providedby the systems and methods described herein, since by the time thehealthcare system identifies an anomaly of infectious disease cases,especially of initially unknown origin such as COVID-19, the associatedoutbreak is already occurring on a large scale and the time period inwhich actions would have needed to be taken to minimize the effects ofthe outbreak has passed.

Furthermore, in accordance with the systems and methods describedherein, not every user must have a particular symptom in order for thenetwork of temperature sensing probes to see and identify the spread ofillness. By way of example, even if only a fraction of users havesymptoms (such as a fever, for example), the network of temperaturesensing probes can still pick up on the growth in that number of peopleto correctly assess transmission rates. Thus, if only 50% of people withCOVID-19, for example, have a fever, but the user data received from thenetwork of temperature sensing probes indicates that the number ofpeople in a particular geography doubles, it can be determined thatCOVID-19 is spreading. The transmission rate can also be assessed andreported based on the user data received from the network of temperaturesensing probes.

As described in more detail below, various embodiments of the presentdisclosure also generally relate to long-lead ILI forecasting based onreal-time data collected from the network of temperature sensing probes.In some embodiments, for example, a 12-week ILI forecast can begenerated for a particular geographic area based on geo-coded datacollected from smart thermometers within the network, as described inmore detail below. Long-lead ILI forecasting in accordance with thepresent disclosure can leverage geo-specific data to estimate theseasonal transmissivity of flu per city, or other type of geographicregion or population node (such as a school, workplace, or othersuitable grouping of users), allowing an influenza transmissionfingerprint to be developed for each geographic area, based on pastinfluenza outbreaks. Geographic areas can have unique epidemic intensitycurves driven by climate and population structure and these patterns canbe used to build highly accurate long-lead ILI forecasts forgeo-specific regions. Forecasts described herein can leverage multipleyears of county-specific incidence data to calculate daily reproductivenumber (R) estimates that are unique to each geographic area using theEquation (1):

I _(t+1) =R _(t)Σ_(k) w _(k) I _(t−k)  (1)

where w is the generation distribution time, I is county-level incidence(as detected by an illness detecting and tracking computing system 100of FIG. 1, described below), R is the reproductive number, and Σ_(k)w_(k)I_(t−k) is effective incidence. Further, w can be estimated basedon findings from literature on the rates of flu spread using a gammadistribution for spread from hosts for 1 to 5 days, with a mean of 2.5days and scale of 0.6 days, for example.

Using Equation (1), R can be estimated for all previous daily timestepsper region and then median R per day of year can be estimated. Thisapproach can provide an influenza transmission fingerprint per localethat can be used to predict future influenza incidence by forwardpropagating I using the same equation above. For forward prediction, thedaily estimates of R can be substituted for all future dates (t) topredict I_(t+1). Finally, measurement uncertainty in the incidence andinfluenza predictions can be accounted for by running an ensemble ofpredictions where random Gaussian noise is added to the starting valuesof I at the point of influenza forecasting. The scale of Gaussian noisecan be determined for each geographic area by estimating the standarddeviation of measurement noise for each geographic area via detrendingthe incidence time-series with a 14-day centered rolling mean. The noiseobserved in the incidence signal is normally distributed and decreaseswith the number of temperature sensing probes per geographic area.

FIG. 1 schematically illustrates an end-to-end illness data collectionand processing system in accordance with one non-limiting embodiment.Such illness data can be utilized to generate long-lead ILI forecasts,outbreak detection, and provide other illness-related signaling inaccordance with the present disclosure. More specifically, the systemcan allow for data collection from individuals immediately from symptomonset, such as via a smart thermometer, a consistently used symptomtracking mobile app, a wearable device, and/or other data acquisitionapproaches. The system can thereby acquire key illness biometric/vitalsign data such as without limitation, temperature, heart rate, and/orrespiratory rate, alongside GPS level coordinates. Via symptom orbiometric illness data, the system can detect illness earlier in thecourse of disease than traditional surveillance mechanisms, since thesystem can detect illness around symptom onset as opposed to the delayof having an individual with worsening symptoms deciding to seek care,being able to access that care and diagnostic testing, and the lag ofrunning and reporting test results. Widespread use of the system bydisparate populations, but especially key sentinel populations that arefrequently among the first impacted by infectious disease spread due toexposures at school, work or home (e.g., children, underservedcommunities, larger multi-generation households, front line workers andfirst responders) can beneficially increase accuracy and robustness ofthe system output.

By way of example, if atypical illness is detected amongst thosesentinel groups that are providing symptomatic or biometric illnessdata, and is detected earlier than through traditional surveillancemechanisms due to the nature of the system, a potential outbreak can bedetected at its early stages before further community spread takesplace. In contrast, due to the lagging nature of care seeking anddiagnostic testing, when existing traditional disease surveillancesystems are used, once atypical levels of illness are detected vialaboratory testing from individuals who sought care, sufficient time haspassed for more widespread disease transmission to occur. Due to theexponential nature of infectious disease transmission, a difference indetection and response time on the magnitude of days can havesignificant ramifications on reducing morbidity, mortality and societalcost of disease. In accordance with various embodiments, the system canleverage multiple types of data inputs, such as multiple biometric datainputs (temperature, heart rate, respiratory rate, and so forth) as wellas symptom inputs. Those data inputs, coupled with knowing which familymember (for calculation of household transmission) provided the data andgrowth in nodes (e.g., schools) yields an optimal system for detectionof outbreaks, forecasting and differentiation from normal/seasonalepidemics.

The system can comprise, for example, a plurality of temperature sensingprobes 114 (such as medical thermometers) that are each communicativelycoupled with a respective auxiliary computing device, such as mobilecomputing devices 112 (e.g. smartphones, tablets, computers, and soforth). The mobile computing devices 112 can be coupled with an illnessdetection and tracking computing system 100 through a communicationnetwork 134. While FIG. 1 and other figures herein depict the use oftemperature sensing probes, this disclosure is not so limited. Instead,the systems and methods described herein are operable using datacollected by any of a variety of biometric collection devices. Thus,while many operational embodiments are described in the context oftemperature-based data collected by a thermometer, other embodiments canutilize data from other types of biometric collection devices, such as,without limitation, pulse oximeters, heart rate monitors, wearablefitness trackers or other types of wearables, and the like. Many suchbiometric collection devices can be utilized by users prior to enteringthe healthcare system (i.e., prior to a doctor's appointment or hospitalvisit), thereby providing timely biometric data to the illness detectingand tracking computing system 100 that would not otherwise be available,or would necessarily be lagging data.

In some embodiments, a user can provide various data related to theuser's health into to the mobile computing devices 112, for example,symptoms, medications taken, vaccinations, or diagnoses. By way ofexample, users can set up a profile with additional contextual dataassociated with each profile, such as the age of the user, gender,school, employer, and other social and demographic data that wouldinform the user's risk of acquiring specific illnesses or for treatmentrecommendations. The profile can be used, for example, to associate theuser with one or more different population nodes. Multiple profiles canbe created per mobile computing device 112. In some embodiments, usersmay indicate that specific profiles belong to the same household orother social group.

Additionally or alternatively, the mobile computing device 112 can beleveraged to collect various biometric data from its user. By way ofexample, heart rate detection can be provided by the mobile computingdevice 112. In some embodiments, the mobile computing device 112 canprovide respiratory rate, or other respiratory-related information tothe illness detecting and tracking computing system 100. Any of avariety of suitable approaches can be used to track heart rate,respiratory rate, or other biometric data using the mobile computingdevice 112, such as using an on-board camera, microphone, or one or morespecialized biometric sensors.

User data, such as temperature readings and/or other biometric data, andin some cases user-entered health information, can be transmitted to theillness detection and tracking computing system 100 by the mobilecomputing device 112. In some cases, temperature sensing probes 114 canbe configured to transmit data directly to the illness detection andtracking computing system 100 without the aid of the mobile computingdevice 112. In yet other embodiments, the user may manually entertemperature data directly into the mobile computing device 112 (i.e.,via a touchscreen interface) that is, in turn, transmitted to theillness detection and tracking computing system 100. In any event, theillness detection and tracking computing system 100 can be configured tostore various types of data transmitted from the mobile computing device112 or the temperature sensing probe 114 in one or more databases 106.The mobile computing device 112 can be further configured to transmit tothe illness detection and tracking computing system 100 one or moregeolocations (e.g., latitude, longitude coordinates), IP addresses, andone or more time measurements. The geolocations can identify thelocation of the individual when taking a temperature or recordingsymptoms, thereby allowing for geographic granularity. The timemeasurements can include the time when the individual was taking atemperature or recording symptoms.

In some embodiments, the illness detection and tracking computing system100 can retrieve other data sets, shown as third party data 130, whichare not generated by the temperature sensing probes 114. For example,the illness detection and tracking computing system 100 can retrieve webdata from the Centers for Disease Control's (CDC's) Weekly U.S.Influenza Surveillance Report, for the purpose of training machinelearning models that identify illness features that distinguishinfluenza from other fever-inducing illnesses.

In accordance with the present disclosure, raw data collected by theillness detection and tracking computing system 100 can be transformedby an analysis engine 107 into illness signals. In one embodiment, theseillness signals are made available for consumption by externalapplications or organizations (e.g., public health system) throughapplication programming interfaces (APIs) 109 accessed by a userapplication 142. In one embodiment, the illness detection and trackingcomputing system 100 can produce a signal indicative of aggregatedcommunity influenza levels for particular geographic areas, for example.Such signal can be used to generate the long-lead ILI forecastingmodels, as well as used to generate outbreak detection and tracking, asdescribed herein.

While influenza forecasting models can be helpful, rapid identificationof emerging epidemics remains a massive challenge. In accordance withthe present disclosure, however, systems and methods are provided todetect for localized illness anomalies for a population node, censustrack, census block, or other geographic region based on the real-timeincidence data collected by the illness detection and tracking computingsystem 100 (FIG. 1). Additionally or alternatively, ILI forecastingmodels can be used to estimate expected illness trends by makingpredictions prior to an expected outbreak of illnesses such as COVID-19,H1N1, SARS, MERS, and so forth. In accordance with some embodiments ofthe present disclosure, the real-time signal generated by the illnessdetection and tracking computing system 100 can be compared to theexpectations of the ILI model for a particular geographic area or othertype of population node. Illness trends that are not likely due tonormal seasonal influenza patterns can be identified such that furtherinvestigation into the anomalous data can be initiated. Thus, real-timeillness levels in a particular population node (i.e., geographic region,city, county, state, school, school system, workplace, etc.), asdetected by the illness detection and tracking computing system 100 canbe compared to the ensemble predictions of expected influenza. Suchcomparison can be used to estimate the likelihood that currentlydetected incidences are due to seasonal influenza dynamics. In someembodiments, any real-time value above an upper 95% confidence intervalof seasonal influenza can be flagged as anomalous. Additionally oralternatively, other characteristics of the real-time illness levels canbe assessed by the illness detection and tracking computing system 100to identify potential infectious disease hotspots. For example, when arate of users with a fever increases above a threshold rate, thepopulation node associated with those users can be flagged as anomalous.In some embodiments, a threshold may be determined by calculating thelevel of expected illness if it were dispersed equally over apopulation. If a certain sub-population node exceeds that level, it canbe flagged as anomalous. Other approaches for determining suitablethresholds can be deployed without departing from the scope of thepresent disclosure.

Referring now to FIG. 2, a plot 150 is provided that shows observedinfluenza-like illnesses 152 for a particular population node based onthe real-time data collected by the illness detection and trackingcomputing system 100 (FIG. 1). The population node can be, for example,a geographic area. An influenza forecast 154 on the plot 150 is themedian of the expected influenza forecast, and the band 158 representsupper and lower 95% confidence intervals. As is to be appreciated, anysuitable confidence intervals can be used. The influenza forecast 154can be a long-lead ILI forecast generated in accordance with the presentdisclosure, or it can be an ILI forecast generated by a third party. Theplot 150 shows numerous outbreak anomalies 156 based on the user datacollected from a network of temperature sensing probes, each of whichexceeds the upper 95% confidence interval. Once the outbreak anomalies156 are detected, the illness detection and tracking computing system100 can flag the incidences for further investigation using any suitablenotification or alerting approach. In one embodiment, illness signalsbased on the outbreak anomalies 156 can be made available forconsumption by external applications or organizations. Examples ofsignals produced can include, without limitation, illness incidence,illness prevalence for a population, effective transmission rates, amongothers.

In accordance with the presently disclosed systems and methods, avariety of visualizations, dashboards, animations, among other types ofdisplays can be generated to convey information regarding ILI forecasts,outbreak anomalies, and so forth. In some embodiments, the illnessdetection and tracking computing system 100 is configured to generatesuch displays, although this disclosure is not so limited. Suchinformation can be displayed based on real-time data, or substantiallyreal-time data (i.e., daily), such as based on the signals generated bythe illness detection and tracking computing system 100.

Referring now to FIG. 3, another example illness detection and trackingcomputing system 200 is depicted. The illness detection and trackingcomputing system 200 is shown in communication with a plurality ofmobile computing devices 212A-N that are members of various populationnodes. In the illustrated example, the population nodes are illustratedas geographic regions 218A-N. Additionally or alternatively, in otherexamples the plurality of mobile computing devices 212A-N can each beassociated with a particular school, school system, campus, workplace,or other environment, grouping, or collection.

As shown in FIG. 3, each mobile computing device 212A-N can becommunicatively coupled to an associated temperature sensing probe214A-N via communications 216A-N. For example, in some embodiments, thecommunications 216A-N utilize a Bluetooth® communications protocol,although this disclosure is not so limited, as any of a variety of wiredor wireless communications 216A-N can be utilized. Each temperaturesensing probe 214A-N can be associated with a user 222A-N, respectively.While FIG. 3 depicts the use of data collected by temperature sensingprobe 214A-N this disclosure is not so limited. As provided above, anysuitable type of biometric collection device can be used withoutdeparting from the scope of the current disclosure.

The example geographic regions 218A-N of FIG. 3 can be any suitableregion, such as a county, a zip code, a state, a country, a metropolitanstatistical area (MSA), among any other suitable demarcation. Further,each of the various geographic regions 218A-N can be formed fromdifferent types of boundaries. For example, the geographic region 218Acan be a city while the geographic region 218B can be a county. In anyevent, a plurality of temperature sensing probes 214A-N can be activelycollecting temperatures of the users 222A-N within the variousgeographic regions 218A-N. In some use cases, each the geographicregions 218A-N can include thousands and thousands of temperaturesensing probes 214A-N. Larger sized geographic regions 218A-N mayinclude hundreds of thousands, or even millions, of temperature sensingprobes 214A-N. Furthermore, as provided above, while FIG. 3 illustratespopulation nodes in the context of geographic regions, the presentlydisclosed systems and methods can provide the functionality to group theusers 222A-N into any of a number of different population nodes.

In some embodiments, and similar to the system of FIG. 1, mobilecommunication devices 212A-N can be in communication with the illnessdetection and tracking computing system 200 via any suitablecommunication network 234. The communication network 234 can include anysuitable computer or data networks, including the Internet, LANs, WANs,GPRS networks, etc., that can comprise wired and/or wirelesscommunication links.

The mobile communication devices 212A-N can be any type of computerdevice suitable for communication with the illness detection andtracking computing system 200 over the communication network 234, suchas a wearable computing device, a mobile telephone, a tablet computer, adevice that is a combination handheld computer and mobile telephone(sometimes referred to as a “smart phone”), a personal computer (such asa laptop computer, netbook computer, desktop computer, and so forth), orany other suitable mobile communications device, such as personaldigital assistants (PDA), tablet devices, gaming devices, or mediaplayers, for example. In some embodiments, the mobile communicationdevices 212A-N can execute a specialized application that provides acommunication channel between the mobile communication devices 212A-Nand the illness detection and tracking computing system 200.Additionally or alternatively, the mobile communication devices 212A-Ncan execute a web browser application that allows the respective user222A-N to interface with the illness detection and tracking computingsystem 200 through web-based communication. In any event, user data220A-N can be transmitted from the mobile communication devices 212A-Nto the illness detection and tracking computing system 200. While thecontents of the user data 220A-N can vary based on implementation, insome embodiments, the user data includes a geolocation 224 (as providedby the mobile communication device 212A-N), a user temperature reading226 (as measured by the temperature sensing probe 214A-N), and a timestamp 228.

Based on the user data 220A-N received from the mobile communicationdevices 212A-N the illness detection and tracking computing system 200can generate illness signals 240 that can be provided to variousrecipient computing systems 242. As is to be appreciated, such illnesssignals 240 can be provided or otherwise conveyed in any suitable formatthrough dashboards, animations, or a variety of other types of displays.For example, the illness signal(s) 240 can be made available forconsumption by external applications or organizations (e.g., publichealth system) through application programming interfaces (APIs). In oneembodiment, for example, the illness detection and tracking computingsystem 200 can produce illness signal(s) 240 that are indicative ofaggregated community influenza levels for each of the geographic regions218A-N. Additionally or alternatively, such illness signal(s) 240 canindicate the presence of a potential contagious illness outbreak in oneor more of the geographic regions 218A-N. In embodiments utilizing otherpopulation nodes besides geographic regions, such signal(s) 240 can begenerated based on the particular population node being surveilled. Assuch, the signal(s) can be indicative of outbreak activity within aparticular school, school system, institution of higher learning, or avariety of other groupings or collections of users.

The illness detection and tracking computing system 200 can be providedusing any suitable processor-based device or system, such as a personalcomputer, laptop, server, mainframe, or a collection (e.g., network) ofmultiple computers, for example. The illness detection and trackingcomputing system 200 can include one or more processors 202 and one ormore computer memory units 204. For convenience, only one processor 202and only one memory unit 204 are shown in FIG. 1. The processor 202 canexecute software instructions stored on the memory unit 204. Theprocessor 202 can be implemented as an integrated circuit (IC) havingone or multiple cores. The memory unit 204 can include volatile and/ornon-volatile memory units. Volatile memory units can include randomaccess memory (RAM), for example. Non-volatile memory units can includeread only memory (ROM), for example, as well as mechanical non-volatilememory systems, such as, for example, a hard disk drive, an optical diskdrive, etc. The RAM and/or ROM memory units can be implemented asdiscrete memory ICs, for example.

The memory unit 204 can store executable software and data for theillness detection and tracking computing system 200. When the processor202 of the illness detection and tracking computing system 200 executesthe software, the processor 202 can be caused to perform the variousoperations of the illness detection and tracking computing system 200.Data used by the illness detection and tracking computing system 200 canbe from various sources, such as a database(s) 206, which can be anelectronic computer database, for example. The data stored in thedatabase(s) 206 can be stored in a non-volatile computer memory, such asa hard disk drive, a read only memory (e.g., a ROM IC), or other typesof non-volatile memory. In some embodiments, one or more databases 206can be stored on a remote electronic computer system, for example. As isto be appreciated, a variety of other databases, or other types ofmemory storage structures, can be utilized or otherwise associated withthe illness detection and tracking computing system 200. Additionally,the illness detection and tracking computing system 200 can use thirdparty data set(s) 230, as may be provided by various third parties. Insome embodiments, the third party data set(s) 230 comprise illness-basedweb data received from a national public health institute, for example.

As shown in FIG. 3, the illness detection and tracking computing system200 can include several computer servers and databases. For example, theillness detection and tracking computing system 200 can include one ormore application servers 208, web servers 210, and/or any other type ofservers. For convenience, only one application server 208 and one webserver 210 are shown in FIG. 3, although it should be recognized thatthe disclosure is not so limited. The servers can cause content to besent to the mobile computing devices 212A-N and/or other recipientcomputing systems 242 in any number of formats, such as text-basedmessages, multimedia message, email messages, smart phone notifications,web pages, and so forth. The servers 208 and 210 can comprise processors(e.g., CPUs), memory units (e.g., RAM, ROM), non-volatile storagesystems (e.g., hard disk drive systems), etc. The servers 208 and 210can utilize operating systems, such as Solaris, Linux, or Windows Serveroperating systems, for example.

The web server 210 can provide a graphical web user interface throughwhich various users of the system can interact with the illnessdetection and tracking computing system 200. The web server 210 canaccept requests, such as HTTP requests, from clients (such as via webbrowsers on the mobile computing devices 212A-N, recipient computingsystem(s) 242, for example), and serve the clients responses, such asHTTP responses, along with optional data content, such as web pages(e.g., HTML documents) and linked objects (such as images, video, and soforth).

The application server 208 can provide a user interface for users who donot communicate with the illness detection and tracking computing system200 using a web browser. Such users can have special software installedon their mobile computing devices 212A-N, and/or recipient computingsystem(s) 242 that allows them to communicate with the applicationserver 208 via the communication network 234. Such software can bedownloaded, for example, from the illness detection and trackingcomputing system 200, or other software application provider, over thecommunication network 234 to such computing devices.

Embodiments of the illness detection and tracking computing system 200can also be implemented in cloud computing environments. “Cloudcomputing” may be defined as a model for enabling ubiquitous,convenient, on-demand network access to a shared pool of configurablecomputing resources (e.g., networks, servers, storage, applications, andservices) that can be rapidly provisioned via virtualization andreleased with minimal management effort or service provider interaction,and then scaled accordingly. A cloud model can be composed of variouscharacteristics (e.g., on-demand self-service, broad network access,resource pooling, rapid elasticity, measured service, etc.), servicemodels (e.g., Software as a Service (“SaaS”), Platform as a Service(“PaaS”), Infrastructure as a Service (“IaaS”), and deployment models(e.g., private cloud, community cloud, public cloud, hybrid cloud,etc.).

Referring now to FIG. 4, another example illness detection and trackingcomputing system 300 is depicted. The illness detection and trackingcomputing system 300 can be similar to the illness detection andtracking computing system 200. As shown, the illness detection andtracking computing system 300 can include, for example, a processor 302,a memory unit 304, a database 306, an application server 308, and a webserver 310. The illness detection and tracking computing system 300 canbe configured to provide illness signal(s) 340 to various recipientcomputing systems 342. As shown, the illness detection and trackingcomputing system 300 can generate, for example, atypical illnessreporting for each of a plurality of geographic regions 318A-N. For thepurposes of illustration, atypical illness rates for each atypicalillness geographic region 318A-N, as determined by the illness detectionand tracking computing system 300, are shown as plots 344A-N. As usedherein, atypical illness can refer to the difference between thereal-time thermometer ILI signal and the 97.5% percentile drawn from aninfluenza forecast ensemble. As is to be appreciated, however, otherpercentiles and/or other approaches for quantifying atypical illness canbe used without departing from the scope of the present disclosure.

The geographic regions 318A-N can be any suitable region, such as acounty, a zip code, a state, a country, a metropolitan statistical area(MSA), and so forth. While geographic regions 318A-N are depicted inFIG. 4 for the purposes of illustration, it is to be appreciated thatthe illness detection and tracking computing system 300 can generatesignaling for a variety of different types of population nodes.

Referring now to FIG. 5, detection of a contagious illness outbreak forgeographic region 318A by the illness detection and tracking computingsystem 300 is schematically illustrated. Determined rates of change 348,350, 352 of fever-induced illness for geographic region 318A areschematically shown. As is to be appreciated, when the determined rateof change increases, the number of atypical incidences over a period oftime are rising. As such, when the determined rate of change offever-induced illness exceeds a threshold rate of change, an outbreaksignal 354 can be generated by the illness detection and trackingcomputing system 300. Additionally, based on the determined rates ofchange 348, 350, 352, the illness detection and tracking computingsystem 300 can be used to determine an effective reproduction rate (Rt).As used herein, reproductive rate (Rt) is defined as the average numberof secondary cases of febrile disease caused by a single febrileindividual over their infectious period. Thus the signaling produced bythe illness detection and tracking computing system 300 can be used tocontinuously provide insights into disease outbreaks, such as alertingto likely future case surges or predicting the magnitude and timing ofpeak cases for a particular population node or grouping of populationnodes.

In some embodiments, observed illness, atypical illness, and atypicaltransmission signals are incorporated by the illness detection andtracking computing system 300 into a classification model that can beused to predict periods of high, continuous contagious illness casegrowth for particular population nodes. These periods of high casegrowth can be defined by periods of high day-over-day growth innormalized case counts, determined from the first difference andcorresponding to periods of high case accumulation during exponentialgrowth. In accordance with various embodiments, the trainedclassification models can predict the target at least two weeks inadvance. This allows the prediction of outbreak events, providing earlywarning at various geographic areas or nodes.

Referring now to FIG. 6, example processing of information received froma plurality of temperature sensing probes 414 by an illness detectionand tracking computing system 400 is depicted. As shown, the temperaturesensing probes 414 can be dispersed amongst a plurality of differentpopulation nodes, shown as geographic regions 418A-N for the purposes ofillustration. Based on user data received from each of the temperaturesensing probes 414 the illness detection and tracking computing system400 can perform various processing. At 402, the illness detection andtracking computing system 400 can track fever counts per distinct userper each geographic region 418A-N. Furthermore, in some embodiments,other additional symptoms of users can be received by the illnessdetection and tracking computing system 400. For example, a user canmanually enter symptoms into a mobile communication device associatedwith the temperature sensing probes 414, and the mobile communicationdevice can transmit the symptom listing to the illness detection andtracking computing system 400. At 404, the illness detection andtracking computing system 400 can determine a daily fever incidencelevel (such as an ILI determination). Next, at 406, the illnessdetection and tracking computing system 400 can determine ILI forecastsfor each geographic region 418A-N. Such ILI forecasts can be based on,for example, the expected Rt for each geographic region. Once the ILIforecast is determined, the illness detection and tracking computingsystem 400 can then monitor for deviations from the forecast. At 408,the illness detection and tracking computing system 400 can identify anatypical ILI in a particular geographic region 418A-N. At 410, theillness detection and tracking computing system 400 can identify anatypical Rt in a particular geographic region 418A-N. Based on theidentification of atypical ILI and/or atypical Rt, appropriate signalingcan be generated by the illness detection and tracking computing system400 and provided to appropriate recipients, such as federal, state,local governments, school officials, and/or healthcare entities forexample.

FIG. 7 provides an example visualization 543 generated by an illnessdetection and tracking computing system 500 that can be presented on arecipient computing system 542. As is to be appreciated, the illnessdetection and tracking computing system 500 can be in communication witha plurality of mobile computing devices, each of which are incommunication with a smart thermometer (as shown in FIG. 3, forexample). The visualization 543 conveys atypical incidence (dashed line)over time and daily confirmed cases of COVID-19 (solid line) over thesame time period. In this illustrated example, the population node is ageographic region. As shown by the visualization 543, the illnessdetection and tracking computing system 500 successfully detected thesurge in daily confirmed cases in the state of New Jersey over two weeksprior to occurrence of the surge. Thus, the illness detection andtracking computing system 500 can be leveraged to accurately detectcommunity spread of contagious illness approximately 2-4 weeks inadvance of laboratory confirmed case surges, depending on laboratorycapacity and test availability. Beneficially, the illness detection andtracking computing system 500 can provide resolution to the county andeven sub-county level in areas with high thermometer penetration. Basedon the valuable information and alerts provided by the illness detectionand tracking computing system 500 actions can be taken, such as theclosure of schools, social distancing mandates, and the like, in aneffort to reduce the impacts of the pending surge. Additionally, testingkits, medicines, and so forth, can be efficiently managed in view of thesurges identified by the illness detection and tracking computing system500.

FIG. 8 provides another example visualization 543 generated by theillness detection and tracking computing system 500 and presented on therecipient computing system 542. In this example visualization 543, theimpact of mobility restrictions on a population over a period of timecan be assessed based on the determined change of atypical Rt over theperiod of time. In the illustrated example for New York, the number ofrouting requests to mapping websites is plotted over time (solid line),with a state of emergency declared on Mar. 7, 2020. Subsequent to thedeclaration of the state of emergency, the number of routing requestsdeclines as the number of drivers decrease. Correlated to thedeclaration of the state of emergency, the atypical Rt (dashed line) isalso shown to similarly decline, thereby confirming that the mobilityrestrictions beneficially impacted Rt for New York.

FIG. 9 provides another example visualization 543 generated by theillness detection and tracking computing system 500 and presented on therecipient computing system 542. In this example visualization 543, thenormalized case velocity over time (dashed line), as determined by theillness detection and tracking computing system 500, is shown in the topplot. Additionally, a cutoff threshold velocity (solid line) is plotted.The crossing of the normalized case velocity above the cutoff thresholdvelocity is indicative of a rapid increase in cases. Based on thisthreshold crossing, appropriate outbreak signaling can be generated. Thebottom plot includes the number of daily confirmed cases over the sameperiod of time, with outbreaks identified. As shown, the identifiedoutbreaks are correlated to points in time that the normalized casevelocity exceeded the cutoff threshold.

FIG. 10 is a flow chart of an example process that can be performed byan illness detection and tracking computing system in accordance withthe present disclosure. At 602, user data from each of a plurality ofmobile computing devices is received by the illness detection andtracking computing system via network communications over a period oftime. The user data received from each of the computing devices cancomprise a geolocation of the computing device, a user temperaturereading as collected by the associated temperature sensing probe, and atime stamp associated with the temperature reading. At 604, for each ofthe plurality of different geographic regions, and based on the userdata received from the mobile computing devices physically locatedwithin each of the different geographic regions, a rate of change offever-induced illness can be determined based on the user data. At 606,an illness signal is generated for each of the different geographicregions based on the determined rate of change of fever-induced illnessfor each of the different geographic regions. At 608, the illness signalfor each of the different geographic regions is graphically conveyed.The illness signal can be a real-time illness signal. At 610, it isdetermined if the rate of change of fever-induced illness for each ofthe different geographic regions is above a threshold rate of change. Ifso, at 612, an outbreak signal can be generated for the associatedgeographic region, or other suitable population node. Otherwise, theprocess can progress to 614, where an effective reproduction rate (Rt)can be determined for each of the different geographic regions based atleast in part on the determined rate of change of fever-induced illnessfor each of the different geographic regions.

The foregoing description of embodiments and examples has been presentedfor purposes of description. It is not intended to be exhaustive orlimiting to the forms described. Numerous modifications are possible inlight of the above teachings. Some of those modifications have beendiscussed and others will be understood by those skilled in the art. Theembodiments were chosen and described for illustration of variousembodiments. The scope is, of course, not limited to the examples orembodiments set forth herein, but can be employed in any number ofapplications and equivalent articles by those of ordinary skill in theart.

1. An illness detection and tracking computing system, comprising: aplurality of temperature sensing probes, wherein each of the temperaturesensing probes is configured to wirelessly communicate with anassociated mobile computing device, and wherein the plurality oftemperature sensing probes are dispersed amongst a plurality ofdifferent population nodes; a centralized illness detection and trackingcomputing system comprising at least one memory and at least oneprocessor, wherein the illness detection and tracking computing systemis in networked communication with each of the associated mobilecomputing devices, wherein the at least one memory stores instructionswhich when executed cause the illness detection and tracking computingsystem to: over a period of time receive user data from each of themobile computing devices via network communications, wherein the userdata received from each of the computing devices comprises a geolocationof the computing device, a user temperature reading as collected by theassociated temperature sensing probe, and a time stamp associated withthe temperature reading; for each of the plurality of differentpopulation nodes, based on the user data received over a period of timefrom the mobile computing devices associated with the differentpopulation nodes, determine a rate of change of fever-induced illnessbased on the user data received from the mobile computing devicesassociated with the population node; generate an illness signal for eachof the different population nodes based on the determined rate of changeof fever-induced illness for each of the different population nodes;compare the determined rate of change of fever-induced illness for eachof the different population nodes to a threshold rate of change; andwhen the determined rate of change of fever-induced illness exceeds thethreshold rate of change, generate an outbreak signal for the associatedpopulation node.
 2. The illness detection and tracking system of claim1, wherein the instructions further cause the illness detection andtracking computing system to: graphically convey the illness signal foreach of the different population nodes, and wherein the illness signalis a real-time illness signal.
 3. The illness detection and trackingcomputing system of claim 1, wherein the instructions further cause theillness detection and tracking computing system to: graphically conveythe outbreak signal for each of the different population nodes.
 4. Theillness detection and tracking computing system of claim 1, wherein theinstructions further cause the illness detection and tracking computingsystem to: determine an effective reproduction rate (Rt) for each of thedifferent population nodes based at least in part on the determined rateof change of fever-induced illness for each of the different populationnodes.
 5. The illness detection and tracking computing system of claim1, wherein the instructions further cause the illness detection andtracking computing system to: receive data sets from one or more thirdparties via network communications.
 6. The illness detection andtracking computing system of claim 5, wherein the data sets compriseillness-based web data received from a national public health institute.7. The illness detection and tracking and system of claim 1, wherein thetemperature sensing probe is a medical thermometer.
 8. The illnessdetection and tracking computing system of claim 1, wherein each of themobile computing devices is any of a smart phone, a tablet computer, alaptop computer, and a desktop computer.
 9. The illness detection andtracking computing system of claim 1, wherein the instructions furthercause the illness detection and tracking computing system to: for eachof the users, store demographic data, wherein the demographic datacomprises one or more of gender data, age data, employment data, andeducational data.
 10. The illness detection and tracking computingsystem of claim 1, wherein the instructions further cause the illnessdetection and tracking computing system to: provide the generatedillness signal for each of the different population nodes to a thirdparty recipient via an application programming interface.
 11. Theillness detection and tracking computing system of claim 1, wherein oneor more of the plurality of population nodes is a geographic region, andwherein the mobile computing devices associated with the geographicregion are physically located within the geographic region.
 12. Theillness detection and tracking computing system of claim 1, wherein oneor more of the plurality of population nodes is a geographic region andthe geographic region is a zip code, census tract, or census block, andwherein the mobile computing devices associated with the geographicregion are physically located within the zip code.
 13. The illnessdetection and tracking computing system of claim 1, wherein one or moreof the plurality of population nodes is a geographic region and thegeographic region is a state, and wherein the mobile computing devicesassociated with the geographic region are physically located within thestate.
 14. The illness detection and tracking computing system of claim1, wherein one or more of the plurality of population nodes is ageographic region and the geographic region is a country, and whereinthe mobile computing devices associated with the geographic region arephysically located within the country.
 15. The illness detection andtracking computing system of claim 1, wherein one or more of theplurality of population nodes is any of school, a school system, or aninstitution of higher learning.
 16. An illness detection and trackingcomputing system, comprising: a plurality of temperature sensing probes,wherein each of the temperature sensing probes is configured tocommunicate with an associated mobile computing device; the illnessdetection and tracking computing system, wherein the illness detectionand tracking computing system is in networked communication with each ofthe associated mobile computing devices, and wherein the illnessdetection and tracking computing system is to: receive user data fromeach of the mobile computing devices via network communications, whereinthe user data received from each of the computing devices comprises ageolocation of the computing device, a user temperature reading ascollected by the associated temperature sensing probe, and a time stampassociated with the temperature reading, wherein each the mobilecomputing devices is associated with a population node; determineincidences of fever-induced illness within the population node based onthe user data received over time from the mobile computing devicesassociated with the population node; and generate an illness signal forthe population node based on the incidences of fever-induced illness forthe population node.
 17. The illness detection and tracking computingsystem of claim 16, wherein the illness detection and tracking computingsystem is to compare a rate of change of fever-induced illness for thepopulation node to a threshold rate of change and, when the rate ofchange of fever-induced illness exceeds the threshold rate of change,generate an outbreak signal for the population node.
 18. The illnessdetection and tracking computing system of claim 16, wherein the illnessdetection and tracking and computing system is to determine an effectivereproduction rate (Rt) for the population node based on the determinedincidences of fever-induced illness within the population node.
 19. Theillness detection and tracking computing system of claim 16, wherein theillness detection and tracking computing system is to store demographicdata for each user, wherein the demographic data comprises one or moreof gender data, age data, employment data, and educational data.
 20. Theillness detection and tracking computing system of claim 16, wherein theillness detection and tracking computing system is to provide thegenerated illness signal for the population node to a third partyrecipient via an application programming interface.
 21. The illnessdetection and tracking computing system of claim 16, wherein thepopulation node is any of a county, a zip code, a census tract, a censusblock, a state, a country, a metropolitan statistical area (MSA), aschool, a school system, and an institution of higher learning.
 22. Theillness detection and tracking computing system of claim 16, wherein thetemperature sensing probe is a medical thermometer.
 23. An illnessdetection and tracking method, comprising: receiving, by an illnessdetection and tracking computing system, user data collected from eachof a plurality of biometric collection devices, wherein the user datareceived comprises a geolocation, a biometric reading as collected bythe biometric collection devices, and a time stamp associated with thebiometric reading; determining, by the illness detection and trackingcomputing system, a biometric rate of change for a population node basedon the user data collected over a period of time by the plurality ofbiometric collection devices for a population node; and generating, bythe illness detection and tracking computing system, an illness signalfor the population node based on the determined biometric rate of changeof for the population node.
 24. The illness detection and trackingmethod of claim 23, further comprising: graphically conveying, by theillness detection and tracking computing system, the illness signal forthe population node, and wherein the illness signal is a real-timeillness signal.
 25. The illness detection and tracking method of claim23, further comprising: comparing, by the illness detection and trackingcomputing system, the determined biometric rate of change for thepopulation node to a threshold rate of change; and when the determinedbiometric rate of change exceeds the threshold rate of change,generating, by the illness detection and tracking computing system, anoutbreak signal for the population node.
 27. (canceled)
 28. (canceled)29. (canceled)
 30. (canceled)
 31. The illness detection and trackingmethod of claim 23, wherein the biometric collection devices arethermometers and the biometric reading is a temperature reading, furthercomprising: determining, by the illness detection and tracking computingsystem, an effective reproduction rate (Rt) for the population nodebased on the determined biometric rate of change illness for thepopulation node.
 32. The illness detection and tracking method of claim23, further comprising: for each of the users, storing, by the illnessdetection and tracking computing system, demographic data, wherein thedemographic data comprises one or more of gender data, age data,employment data, and educational data.
 33. The illness detection andtracking method of claim 23, wherein each of the biometric collectiondevices is any of a thermometer, a pulse oximeter, a heart rate monitor,and a wearable fitness tracker.
 34. The illness detection and trackingmethod of claim 23, wherein each of the biometric collection devices isa wearable fitness tracker.
 35. The illness detection and trackingmethod of claim 23, wherein the population node is any of a county, azip code, a census tract, a census block a state, a country, a school, aschool system, and an institution of higher learning.